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Python framework for fast Vector Space Modelling

Project description

Gensim is a Python library for Vector Space Modelling with very large corpora. Target audience is the Natural Language Processing (NLP) community.

Features

  • All algorithms are memory-independent w.r.t. the corpus size (can process input larger than RAM),
  • Intuitive interfaces
    • easy to plug in your own input corpus/datastream (trivial streaming API)
    • easy to extend with other Vector Space algorithms (trivial transformation API)
  • Efficient implementations of popular algorithms, such as online Latent Semantic Analysis, Latent Dirichlet Allocation or Random Projections
  • Distributed computing: can run Latent Semantic Analysis and Latent Dirichlet Allocation on a cluster of computers.
  • Extensive HTML documentation and tutorials.

If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia.

Installation

This software depends on NumPy and Scipy, two Python packages for scientific computing. You must have them installed prior to installing gensim.

The simple way to install gensim is:

sudo easy_install gensim

Or, if you have instead downloaded and unzipped the source tar.gz package, you’ll need to run:

python setup.py test
sudo python setup.py install

For alternative modes of installation (without root priviledges, development installation, optional install features), see the documentation.

This version has been tested under Python 2.5 and 2.6, but should run on any 2.5 <= Python < 3.0.

Documentation

Manual for the gensim package is available in HTML. It contains a walk-through of all its features and a complete reference section. It is also included in the source distribution package.


Gensim is open source software, and has been released under the GNU LPGL license. Copyright (c) 2010 Radim Rehurek

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Files for gensim, version 0.7.4
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Filename, size gensim-0.7.4.tar.gz (462.6 kB) File type Source Python version None Upload date Hashes View hashes

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